Cancer classification using deep learning techniques and multi-omics data integration

Abstract

Cancer is one of the most urgent issues in healthcare and medical research. Traditional techniques to cancer categorization frequently fall short of the accuracy required for accurate diagnosis and therapy planning. In recent years, the integration of multi-omics data and the use of deep learning algorithms have emerged as potential solutions for improving cancer classification accuracy and improving our knowledge of the complicated biological pathways that drive cancer. This article provides a thorough examination of the use of deep learning approaches for cancer classification utilising integrated multi-omics data. We use a variety of omics data sources, including genomes, transcriptomics, epigenomics, proteomics, and metabolomics, to provide a comprehensive picture of the cancer molecular landscape. This multi-omics technique enables us to identify subtle molecular fingerprints and biomarkers that are sometimes overlooked when analysing individual data types.To successfully understand complex patterns and correlations within multi-omics data, our deep learning system incorporates convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected neural networks (FCNs).